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np_glove.py
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np_glove.py
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import itertools
import numpy as np
import pandas as pd
import random
import sys
import utils
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
class GloVe:
def __init__(self,
n=100,
xmax=100,
alpha=0.75,
max_iter=100,
eta=0.05,
tol=1e-5,
display_progress=True):
"""
Basic GloVe. This is mainly here as a reference implementation.
We recommend using `torch_glove.py` instead.
Parameters
----------
df : pd.DataFrame or np.array
This must be a square matrix.
n : int (default: 100)
The dimensionality of the output vectors.
xmax : int (default: 100)
Words with frequency greater than this are given weight 1.0.
Words with frequency under this are given weight (c/xmax)**alpha
where c is their count in mat (see the paper, eq. (9)).
alpha : float (default: 0.75)
Exponent in the weighting function (see the paper, eq. (9)).
max_iter : int (default: 100)
Number of training epochs.
eta : float (default: 0.05)
Controls the rate of SGD weight updates.
tol : float (default: 1e-4)
Stopping criterion for the loss.
display_progress : bool (default: True)
Whether to print iteration number and current error to stdout.
"""
self.n = n
self.xmax = xmax
self.alpha = alpha
self.max_iter = max_iter
self.eta = eta
self.tol = tol
self.display_progress = display_progress
def fit(self, df):
"""
Learn the GloVe matrix.
Parameters
----------
df : pd.DataFrame or np.array, shape `(n_vocab, n_vocab)`
This should be a matrix of (possibly scaled) co-occcurrence
counts.
Returns
-------
pd.DataFrame or np.array, shape `(n_vocab, self.n)`
The type will be the same as the user's `df`. If it's a
`pd.DataFrame`, the index will be the same as `df.index`.
"""
X = self.convert_input_to_array(df)
m = X.shape[0]
# Parameters:
W = utils.randmatrix(m, self.n) # Word weights.
C = utils.randmatrix(m, self.n) # Context weights.
B = utils.randmatrix(2, m) # Word and context biases.
# Precomputable GloVe values:
X_log = utils.log_of_array_ignoring_zeros(X)
X_weights = (np.minimum(X, self.xmax) / self.xmax)**self.alpha # eq. (9)
# Learning:
indices = list(range(m))
for iteration in range(self.max_iter):
epoch_error = 0.0
random.shuffle(indices)
for i, j in itertools.product(indices, indices):
if X[i, j] > 0.0:
weight = X_weights[i,j]
# Cost is J' based on eq. (8) in the paper:
diff = W[i].dot(C[j]) + B[0, i] + B[1, j] - X_log[i, j]
fdiff = diff * weight
# Gradients:
wgrad = fdiff * C[j]
cgrad = fdiff * W[i]
wbgrad = fdiff
wcgrad = fdiff
# Updates:
W[i] -= self.eta * wgrad
C[j] -= self.eta * cgrad
B[0, i] -= self.eta * wbgrad
B[1, j] -= self.eta * wcgrad
# One-half squared error term:
epoch_error += 0.5 * weight * (diff**2)
epoch_error /= m
if epoch_error <= self.tol:
utils.progress_bar(
"Converged on iteration {} with error {}".format(
iteration, epoch_error, self.display_progress))
break
utils.progress_bar(
"Finished epoch {} of {}; error is {}".format(
iteration, self.max_iter, epoch_error, self.display_progress))
# Return the sum of the word and context matrices, per the advice
# in section 4.2:
G = W + C
self.embedding = self.convert_output(G, df)
return self.embedding
def score(self, X):
"""
The goal of GloVe is to learn vectors whose dot products are
proportional to the log co-occurrence probability. This score
method assesses that directly using the current `self.embedding`.
Parameters
----------
X : pd.DataFrame or np.array, shape `(self.n_words, self.n_vocab)`
The original count matrix.
Returns
-------
float
The Pearson correlation.
"""
X = self.convert_input_to_array(X)
G = self.convert_input_to_array(self.embedding)
mask = X > 0
M = G.dot(G.T)
X_log = utils.log_of_array_ignoring_zeros(X)
row_log_prob = np.log(X.sum(axis=1))
row_log_prob = np.outer(row_log_prob, np.ones(X.shape[1]))
prob = X_log - row_log_prob
return np.corrcoef(prob[mask].ravel(), M[mask].ravel())[0, 1]
def convert_input_to_array(self, X):
if isinstance(X, pd.DataFrame):
X = X.values
return X
@staticmethod
def convert_output(X_pred, X):
if isinstance(X, pd.DataFrame):
X_pred = pd.DataFrame(X_pred, index=X.index)
return X_pred
def simple_example():
utils.fix_random_seeds()
X = np.array([
[4., 4., 2., 0.],
[4., 61., 8., 18.],
[2., 8., 10., 0.],
[0., 18., 0., 5.]])
mod = GloVe(n=2, max_iter=1000)
print(mod)
G = mod.fit(X)
print("\nLearned vectors:")
print(G)
print("We expect the dot product of learned vectors "
"to be proportional to the log co-occurrence probs. "
"Let's see how close we came:")
corr = mod.score(X)
print("Pearson's R: {} ".format(corr))
return corr
if __name__ == '__main__':
simple_example()